A comprehensive study on the radiomic score derived from perineural invasion in gastric cancer and its correlation with the overall survival of patients.

[BACKGROUND] Perineural invasion (PNI) is closely related to the prognosis of gastric cancer (GC) patients. However, a noninvasive tool for accurately and reliably predicting the PNI is lacking.

[METHODS] The clinical and imaging data of 278 patients from institution I and 39 patients from institution II were retrospectively analyzed. Radiomic features were extracted from the intratumoral and peritumoral regions. Seven independent machine learning (ML) algorithms are used to develop the models. Kaplan-Meier survival analysis and Cox proportional hazards analysis were carried out to compare 3-year and 5-year overall survival (OS) differences among various subgroups based on PNI and radiomic scores.

[RESULTS] T stage and lymphovascular invasion (LVI) were significantly correlated with the PNI (P < 0.01). The OS of patients with different PNI status was significantly different (P < 0.05). Gradient boosting tree is the best ML algorithm. The area-under-the-curve (AUC) values of the optimal radiomics model in the internal test set and external test set were 0.901 and 0.886, respectively. After the introduction of clinical variables T stage and LVI, the performance of the model further improved in predicting the PNI of GC patients, with the AUC of 0.904 in the internal test set and 0.886 in the external test set. The difference in 3-year OS (P = 0.005) and 5-year OS (P = 0.015) among patients with varying radiomic scores was statistically significant.

[CONCLUSION] Radiomics combined with intratumoral and peritumoral features is feasible for evaluating the PNI of GC patients. The prognosis of patients with different radiomic scores was statistically significant.